Optimal Stabilizer Testing and Learning with Limited Quantum Memory
The paper investigates testing and learning unknown n-qubit stabilizer states when limited quantum memory is available (k qubits). It shows that coherent quantum memory separates these tasks, leading to distinct sample complexities: testing requires Θ(n - k) samples, while non-adaptive learning needs O(n^2 / k) samples. This establishes a resource-dependent trade-off.